Coupling graph neural networks and travel mode choice for human mobility prediction

Author:

Wang Kun,Peng Zhenghong,Cai Meng,Wu Hao,Liu Lingbo,Sun Zhihao

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference75 articles.

1. Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction;Zhao;Comput. Environ. Urban,2022

2. Exploring human mobility patterns and travel behavior: a focus on private cars;Xiao;Ieee Intel. Transp. Sy,2022

3. Identifying borders of activity spaces and quantifying border effects on intra-urban travel through spatial interaction network;Jin;Comput. Environ. Urban,2021

4. CN, The total number of motor vehicles nationwide has surpassed 400 million, in, 2022.

5. CN, 2021 Central city passenger traffic: Beijing back on top, Shenzhen Guangzhou metro most crowded, in, 2021.

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